Surface-Enhanced Raman Scattering Bioimaging with an Ultrahigh Signal-to-Background Ratio under Ambient Light

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Single-Injection Composite Tracer Achieves Intraoperative Dual-Tracing and Precise Localization of Sentinel Lymph Nodes

The use of dual-tracer contrast agents in clinical applications, such as sentinel lymph node (SLN) identification, offers significant advantages including enhanced accuracy, sensitivity, as well as comprehensive and multimodal visualization. In the current clinical practice, SLNs are typically marked prior to surgical resection by multiple and sequential injections of two tracers, the radioactive tracer and methylene blue (MB) dye. This imposes physical and psychological burden on patients and medical staff. Surface-enhanced Raman scattering (SERS) nanotags have emerged as promising SLN tracers due to their high sensitivity and specificity. In this study, we propose a novel single-injection composite tracer consisting of SERS nanotags and MB dye solution, to achieve the accurate intraoperative visualization and localization of SLNs. Laser excitation at the second near-infrared window (1064 nm) minimizes the MB fluorescence background interference, allowing the integration of SERS nanotags with MB solution to form the composite tracer, bridging two distinctive but complementary optical modalities. The feasibility of the composite tracer is demonstrated for SLN navigation on rabbit models. For the first time, we successfully visualize and localize multiple SLNs in the axilla of rhesus monkeys. Our study demonstrates the potential of combining MB with SERS nanotags for SLN navigation as the composite tracer, making a significant advancement toward the SLN biopsy in clinical applications.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

NIR-II Surface-Enhanced Raman Scattering Nanoprobes in Biomedicine: Current Impact and Future Directions

The field of second near-infrared (NIR-II) surface-enhanced Raman scattering (SERS) nanoprobes has made commendable progress in biomedicine. This article reviews recent advances and future development of NIR-II SERS nanoprobes. It introduces the fundamental principles of SERS nanoprobes and highlights key advances in the NIR-II window, including reduced tissue attenuation, deep penetration, maximized allowable exposure, and improved photostability. The discussion of future directions includes the refinement of nanoprobe substrates, emphasizing the tailoring of optical properties of metallic SERS-active nanoprobes, and exploring non-metallic alternatives. The intricacies of designing Raman reporters for the NIR-II resonance and the potential of these reporters to advance the field are also discussed. The integration of artificial intelligence (AI) into nanoprobe design represents a cutting-edge approach to overcome current challenges. This article also examines the emergence of deep Raman techniques for through-tissue SERS detection, toward NIR-II SERS tomography. It acknowledges instrumental advancements like improved charge-coupled device sensitivity and accelerated imaging speeds. The article concludes by addressing the critical aspects of biosafety, ease of functionalization, compatibility, and the path to clinical translation. With a comprehensive overview of current achievements and future prospects, this review aims to illuminate the path for NIR-II SERS nanoprobes to innovate diagnostic and therapeutic approaches in biomedicine.

Surface-Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues

Surface-enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate preparation that enhances Raman signal strength on a global scale. To systematically solve these problems, artificial intelligence techniques are applied to analyze the signals of pesticides based on 3D and dynamic SERS imaging. Utilizing perovskite/silver nanoparticles composites (CaTiO3/Ag@BONPs) as enhanced substrates, enabling it not only to cleanse pesticide residues from the surface to pulp of fruits and vegetables, but also to investigate the penetration dynamics of an array of pesticides (chlorpyrifos, thiabendazole, thiram, and acetamiprid). The findings challenge existing paradigms, unveiling a previously unnoticed weakening process during pesticide invasion and revealing the surprising permeability of non-systemic pesticides. Of particular note is easy to overlook that the combined application of pesticides can inadvertently intensify their invasive capacity due to pesticide interactions. The innovative study delves into the realm of pesticide penetration, propelling a paradigm shift in the understanding of food safety. Meanwhile, this strategy provides strong support for the cutting-edge application of SERS imaging technology and also brings valuable reference and enlightenment for researchers in related fields.

Sentinel lymph node identification using NIR-II ultrabright Raman nanotags on preclinical models

Surface-enhanced Raman spectroscopy (SERS) nanotags have garnered much attention as promising bioimaging contrast agent with ultrahigh sensitivity, but their clinical translation faces challenges including biological and laser safety. As breast sentinel lymph node (SLN) imaging agents, SERS nanotags used by local injection and only accumulation in SLNs, which were removed during surgery, greatly reduce biological safety concerns. But their clinical translation lacks pilot demonstration on large animals close to humans. The laser safety requires irradiance below the maximum permissible exposure threshold, which is currently not achievable in most SERS applications. Here we report the invention of the core-shell SERS nanotags with ultrahigh brightness (1 pM limit of detection) at the second near-infrared (NIR-II) window for SLN identification on pre-clinical animal models including rabbits and non-human primate. We for the first time realize the intraoperative SERS-guided SLN navigation under a clinically safe laser (1.73 J/cm2) and identify multiple axillary SLNs on a non-human primate. No evidence of biosafety issues was observed in systematic examinations of these nanotags. Our study unveils the potential of NIR-II SERS nanotags as appropriate SLN tracers, making significant advances toward the accurate positioning of lesions using the SERS-based tracer technique.

A dual-mode lateral flow immunoassay by ultrahigh signal-to background ratio SERS probes for nitrofurazone metabolites ultrasensitive detection

In this work, an ultra-sensitive lateral flow immunoassay (LFIA) with SERS/colorimetric dual signal mode was constructed for the detection of nitrofurazone metabolites, an antibiotic prohibited in animal-origin foods. Au@4-MBN@AgNRs nano-sandwich structural signal tag integrates the unique advantages of high signal-to-background ratio and anti-matrix interference through geometric control of SERS tag and nanoengineering adjustment of chemical composition. Under the optimal conditions, the detection limits of nitrofurazone metabolites by SERS/colorimetric dual-mode LFIA were 20 pg/mL (colorimetric mode) and 0.08 pg/mL (SERS mode). Excitingly, the vLOD of the colorimetric signal improved by a factor of 100 compared to Au NPs-based LFIA. In this study, the proposed dual-mode LFIA was successfully applied to the on-site real-time detection of honey, milk powder, and chicken. It is anticipated that with low background interference and anti-matrix interference output signal, our proposed dual-mode strategy can pave an innovative pathway for the fabrication of a powerful biosensor.

Growing Gold Nanostars on 3D Hydrogel Surfaces

Nanocomposites comprising hydrogels and plasmonic nanoparticles are attractive materials for tissue engineering, bioimaging, and biosensing. These materials are usually fabricated by adding colloidal nanoparticles to the uncured polymer mixture and thus require time-consuming presynthesis, purification, and ligand-exchange steps. Herein, we introduce approaches for rapid synthesis of gold nanostars (AuNSt) in situ on hydrogel substrates, including those with complex three-dimensional (3D) features. These methods enable selective AuNSt growth at the surface of the substrate, and the growth conditions can be tuned to tailor the nanoparticle size and density (coverage). We additionally demonstrate proof-of-concept applications of these nanocomposites for SERS sensing and imaging. High surface coverage with AuNSt enabled 1-2 orders of magnitude higher SERS signals compared to plasmonic hydrogels loaded with premade colloids. Importantly, AuNSt can be prepared without the addition of any potentially cytotoxic surfactants, thereby ensuring a high biocompatibility. Overall, in situ growth becomes a versatile and straightforward approach for the fabrication of plasmonic biomaterials.

Python-assisted detection and photothermal inactivation of Salmonella typhimurium and Staphylococcus aureus on a background-free SERS chip

In this study, a background-free surface-enhanced Raman scattering (SERS) chip with a sandwich configuration was fabricated to enable reliable detection and photothermal inactivation of multiple bacteria. The SERS chip consists of a graphene-coated, phenylboronic-modified plasmonic gold substrate (pAu/G/PBA), and two aptamer-functionalized core (gold)-shell (Prussian blue/Poly-L-lysine and 4-mercaptobenzonitrile/polydopamine) SERS tags (Au@PB@PLL@Apt and Au@MB@PDA@Apt). The detection signals rely on the characteristic and nonoverlapping Raman bands of the SERS tags within the Raman-silent region (1800-2800 cm-1), where no background signals from the sample matrix are observed, leading to improved detection sensitivity and accuracy. Considering the relatively large size of bacteria (e.g., micron level), a rapid Raman mapping technique was chosen over conventional point-scan methods to achieve more reliable quantitative analysis of bacteria. This technique involves collecting and analyzing intensity signals of SERS tags from all the scattering points with an average ensemble effect, which is facilitated by the use of Python. As a proof-of-concept, model bacterium of Salmonella typhimurium and Staphylococcus aureus were successfully detected using the SERS chip with a dynamic range of 10-107 CFU/mL. Additionally, the SERS chip demonstrated successful detection of these bacteria in whole blood samples. Moreover, the photothermal effect of pAu/G led to efficient bacteria elimination, achieving approximately 100% eradication. This study integrated a background-free SERS chip with a Python-assisted rapid Raman mapping technique, resulting in a reliable, rapid and accurate method for detecting and eliminating multiple bacteria, which may provide a promising alternative for multiple screening of bacteria in real samples.

A target-triggered fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular telomerase activity

Telomerase (TE) is a promising diagnostic and prognostic biomarker for many cancers. Quantification of TE activity in living cells is of great significance in biomedical and clinical research. Conventional fluorescence-based sensors for quantification of intracellular TE may suffer from problems of fast photobleaching and auto-fluorescence of some endogenous molecules, and hence are liable to produce false negative or positive results. To address this issue, a fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular TE was designed by functionalizing a bimetallic Au@Ag nanostructure with 4-p-mercaptobenzoic acid (internal standard SERS tag) and a DNA hybrid complex consisted of a telomerase primer strand and its partially complimentary strand modified with Rhodamine 6G. The bimetallic Au@Ag nanostructure serves as an excellent SERS-enhancing and fluorescence-quenching substrate. Intracellular TE will trigger the extension of the primer strand and cause the shedding of Rhodamine 6G-modified complimentary strand from the nano-system through intramolecular DNA strand displacement, resulting in the recovery of the fluorescence of Rhodamine 6G and decrease in its SERS signal. Both the fluorescence of R6G and the ratio between the SERS signals of 4-p-mercaptobenzoic acid and Rhodamine 6G can be used for in situ imaging of intracellular TE. Experimental results showed that the proposed nano-system was featured with low background, excellent cell internalization efficiency, good biocompatibility, high sensitivity, good selectivity, and robustness to false positive results. It can be used to distinguish cancer cells from normal ones, identify different types of cancer cells, as well as perform absolute quantification of intracellular TE, which endows it with great potential in clinical diagnosis, target therapy and prognosis of cancer patients.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Fluorescence-free bis(dithiolene)nickel dyes for surface-enhanced resonance Raman imaging in the second near-infrared window

Second near-infrared window (NIR-II, 1000-1700 nm) imaging is one of the foremost optical imaging techniques. However, surface-enhanced Raman scattering (SERS)-based research in this optical region remains in its infancy, mainly because of a lack of suitable NIR-II Raman reporters. Herein, we report the first example of a nickel dithiolene complex as a NIR-II resonance Raman reporter with intense long wavelength absorption (ε = 9.58 × 104 m-1 cm-1 at 1007 nm), fluorescence-free features and ultrahigh affinity to noble metal surfaces with its eight sulfur atoms. Surface-enhanced resonance Raman scattering nanoprobes constructed with such reporters enable high contrast and highly photostable lymph node imaging far superior to that possible with existing NIR-I and NIR-II SERS nanoprobes. The developed NIR-II nanoprobes allow deep optical penetration (8 mm) as well as in vivo SERS detection of deep-seated microtumors in mice.

Non-Invasive Detection, Precise Localization, and Perioperative Navigation of In Vivo Deep Lesions Using Transmission Raman Spectroscopy

Non-invasive detection and precise localization of deep lesions have attracted significant attention for both fundamental and clinical studies. Optical modality techniques are promising with high sensitivity and molecular specificity, but are limited by shallow tissue penetration and the failure to accurately determine lesion depth. Here the authors report in vivo ratiometric surface-enhanced transmission Raman spectroscopy (SETRS) for non-invasive localization and perioperative surgery navigation of deep sentinel lymph nodes in live rats. The SETRS system uses ultrabright surface-enhanced Raman spectroscopy (SERS) nanoparticles with a low detection limit of 10 pM and a home-built photosafe transmission Raman spectroscopy setup. The ratiometric SETRS strategy is proposed based on the ratio of multiple Raman spectral peaks for obtaining lesion depth. Via this strategy, the depth of the phantom lesions in ex vivo rat tissues is precisely determined with a mean-absolute-percentage-error of 11.8%, and the accurate localization of a 6-mm-deep rat popliteal lymph node is achieved. The feasibility of ratiometric SETRS allows the successful perioperative navigation of in vivo lymph node biopsy surgery in live rats under clinically safe laser irradiance. This study represents a significant step toward the clinical translation of TRS techniques, providing new insights for the design and implementation of in vivo SERS applications.

Electric field-induced assembly of Au-Ag alloy nanoparticles into nano-reticulation for ultrasensitive SERS

This paper discusses a method for assembling Au-Ag alloy nanoparticles (NPs) using direct current (DC) electric field to fabricate highly active SERS substrates. Different nanostructures could be obtained by regulating the intensity and action time of DC electric field. Under the condition of 5mA*10 min, we obtained Au-Ag alloy nano-reticulation (ANR) substrate with excellent SERS activity (Enhancement factor on order of magnitude of 10⁶). ANR substrate has excellent SERS performance due to the resonance matching between its LSPR mode and excitation wavelength. The uniformity of the Raman signal on ANR is greatly improved than bare ITO glass. ANR substrate also has the ability to detect multiple molecules: ANR substrate can respectively detect Rh6G and CV molecules with a concentration as low as 10^-10 M and 10^-9 M and the Raman spectral intensity of the probe molecules on the surface of the ANR substrate has good linear correlation with the molecular concentration (R²> 0.95). In addition, ANR substrate can detect both thiram and aspartame (APM) molecules far below (thiram for 0.0024 ppm and APM for 0.0625 g/L) the safety standard, which demonstrate its practical application potential.

In Vivo Surface-Enhanced Transmission Raman Spectroscopy under Maximum Permissible Exposure: Toward Photosafe Detection of Deep-Seated Tumors

The detection of deep-seated lesions is of great significance for biomedical applications. However, due to the strong photon absorption and scattering of biological tissues, it is challenging to realize in vivo deep optical detections, particularly for those using the safe laser irradiance below clinical maximum permissible exposure (MPE). In this work, the combination of ultra-bright surface-enhanced Raman scattering (SERS) nanotags and transmission Raman spectroscopy (TRS) is reported to achieve the non-invasive and photosafe detection of "phantom" lesions deeply hidden in biological tissues, under the guidance of theoretical calculations showing the importance of SERS nanotags' brightness and the expansion of laser beam size. Using a home-built TRS system with a laser power density of 0.264 W cm^-2 (below the MPE criteria), we successfully demonstrated the detection of SERS nanotags through up to 14-cm-thick ex vivo porcine tissues, as well as in vivo imaging of "phantom" lesions labeled by SERS nanotags in a 1.5-cm-thick unshaved mouse under MPE. This work highlights the potential of transmission Raman-guided identification and non-invasive imaging toward clinically photosafe cancer diagnoses.